A practical guide to Atlassian Rovo Agent Scenarios

Let's be honest, AI agents are popping up everywhere, promising to take over the boring, repetitive tasks so your team can focus on what matters. Atlassian is in on the action with Rovo, its set of AI tools that includes customizable "agents." The key to making these agents do what you want is understanding Rovo Agent Scenarios.

But while scenarios give you some control, they can also be a headache to manage. This guide will walk you through what they are, how they work, and their real-world limits. We'll cover the setup and help you figure out if this is the right tool for you, or if you need something a bit more flexible.

What are Atlassian Rovo Agent Scenarios?

So, what exactly are we talking about? Atlassian Rovo agents are AI helpers built to live inside your Atlassian tools, like Jira and Confluence. You can grab pre-made ones from their marketplace or build your own to do things like summarize docs or draft release notes.

Rovo Agent Scenarios are basically the playbooks that tell an agent what to do in certain situations. Think of it as giving your AI different "hats" to wear for different jobs. Here are the main terms you'll see:

• Scenario: A specific playbook you give an agent, complete with instructions, skills, and knowledge for one type of task.

• Default Scenario: This is the agent’s general-purpose mode. It’s the fallback the agent uses when a more specific scenario isn’t triggered.

• Trigger: The condition that tells the agent to switch hats and use a specific scenario. This could be a phrase a user types or even their tone.

• Instructions: The detailed prompt that outlines the agent’s goals, personality, and what it needs to do within that scenario.

When a Rovo agent receives a prompt, it quickly checks if it matches any of your custom triggers. If it finds one, it runs that specific scenario. If not, it just falls back on its default instructions.

How to set up and manage Rovo Agent Scenarios

Setting up scenarios is how you teach your Rovo agent its special skills. You'll start with a general-purpose "default" scenario and then layer on more specific ones for particular tasks, creating a sort of decision tree for your AI.

Starting with the default scenario

When you first create a Rovo agent, the initial instructions you write become its default scenario. This is its core personality and general knowledge. It's really important to get this right because it’s the catch-all for any request that doesn't fit a specific category. During this step, you’ll define its main purpose, connect it to knowledge sources like certain Confluence pages, and set its overall tone of voice.

Layering on specific scenarios with custom triggers

This is where you can get more specific. You can create extra scenarios that only switch on when certain conditions are met. This allows the agent to handle particular jobs with a unique set of instructions, completely separate from its default programming.

For instance, you could set up:

• A "Bug Triage" scenario that gets triggered by phrases like "report a bug" or "something is broken."

• A "Release Notes" scenario that activates when a user asks to "summarize the latest release."

• A "Customer Feedback" scenario that turns on if it detects a frustrated tone in a user's message.

To do this, you name the new scenario, write a trigger (the thing that activates it), and then give it a unique set of instructions and knowledge sources just for that job.

But this is also where things can get complicated. Every new scenario adds another layer of instructions and triggers you have to keep track of. If you're looking for a simpler way to handle this, tools like eesel AI offer a different path. Instead of juggling separate scenarios, you get a single, unified workflow engine where you can set automation rules and custom actions without creating a web of different instruction sets. It can make scaling your AI a lot less painful.

A workflow diagram that illustrates how a specialized tool like eesel AI automates the customer support process from ticket analysis to resolution.

The limitations of Rovo Agent Scenarios

The idea of giving your AI different "modes" sounds great in theory, but in practice, it can run into some real-world problems, especially as you try to do more with it.

1. Rovo Agent Scenarios get messy, fast

Managing a few scenarios is no big deal. But what about when you have ten, twenty, or even fifty? Each one has its own instructions, triggers, and knowledge sources. This can quickly turn into a tangled web that's hard to maintain, test, and fix when something goes wrong. If triggers overlap, the agent might behave in weird, unpredictable ways, and your team could end up spending more time managing the AI than getting work done with it.

2. Inflexible triggers can miss the mark

Rovo’s triggers are mostly based on keywords or user sentiment. That sounds fine, but it can be surprisingly rigid. A customer might describe a problem using words you didn't think of. When that happens, the question gets punted to the default scenario, which might not have the right information or skills to actually help. This inflexibility can lead to inconsistent or unhelpful AI responses that just frustrate users and create more manual work for your team.

3. You'll need a developer for the cool stuff

The built-in actions for Rovo agents are pretty basic. If you want to do anything more advanced, like connecting to other systems or performing actions with multiple steps, you have to use the Atlassian Forge platform. This immediately creates a dependency on developers, which can be a huge bottleneck for non-technical support or IT teams who just want to build and tweak their AI assistants on the fly.

This is where a tool like eesel AI takes a different approach. It’s built for non-technical folks. You can connect your helpdesk, define custom actions that call external APIs, and build out pretty advanced workflows all from a simple dashboard, no coding needed.

4. Walled-off knowledge and tricky testing

In Rovo, you often have to assign knowledge sources to each specific scenario. This can create silos where knowledge isn't shared, leading to a lot of repeated work. Even worse, testing how all these different scenarios work together is a massive headache. It's tough to know for sure how the agent will respond in every possible situation before you let it loose on your customers.

To get around this, eesel AI automatically pulls all your knowledge sources into one place. It also has a really useful simulation mode where you can test your AI on thousands of past tickets. This way, you can see exactly how it would have performed and make adjustments with confidence before it ever talks to a real customer.

A screenshot of the eesel AI simulation dashboard showing how AI uses past product knowledge to predict future support automation rates.

Atlassian Rovo Agent Scenarios pricing

So, how much does Rovo cost? Well, you can't just buy it on its own. Rovo's features, including the agents, are part of the Premium and Enterprise plans for Atlassian products like Jira, Confluence, and Jira Service Management.

This means there's no line item on your bill for Rovo itself. The cost is just baked into your bigger Atlassian subscription. To get Rovo, you have to upgrade to one of the more expensive tiers, which can be a pretty big jump from the Standard plans.

Other costs to consider

Now, "included" is a nice word, but there are a few other costs to keep in mind:

• You might need to upgrade everyone: You may have to move your entire organization to a Premium plan just to get the AI features, even if most people don't need the other premium perks.

• The cost of developers: Like we mentioned, any advanced customization means you'll need developer time to work with Atlassian Forge, which adds to the overall cost.

• The time spent managing it all: The hours your team spends building, testing, and untangling a complex web of scenarios is a real operational cost.

A more transparent alternative

This is a big difference from dedicated AI platforms that have more straightforward pricing. For instance, eesel AI's pricing is based on the features you actually need and how much you use it, with clear monthly and annual options. A key detail is that eesel AI doesn't charge you per resolution, so you won't get a surprise bill after a busy month. That predictability makes it way easier to budget and figure out the return on your investment.

A visual of the eesel AI pricing page, which contrasts with opaque models by showing clear, public-facing costs for Rovo Agent Scenarios alternatives.

Are Rovo Agent Scenarios right for you?

So, what's the verdict? Are Rovo Agent Scenarios right for your team? If you're already deep in the Atlassian world on a Premium or Enterprise plan and your automation needs are pretty straightforward, it can be a solid place to start. It's built right in, which is convenient.

But, the scenario-based system has its drawbacks when it comes to scaling, management, and just general flexibility. The clunky triggers, the need for developers for advanced stuff, and the tough testing process mean it might not keep up with a fast-moving support or IT team. If you're feeling like you might hit those limits, it's probably worth looking at a dedicated tool.

A more flexible path to AI automation

If you're after an AI tool that gives you more control without the extra complexity, take a look at eesel AI. It connects to all the tools you already use (not just Atlassian products), learns from your team's past work, and lets you build powerful, custom automations without a massive project.

With its simulation engine and clear pricing, you can get a real handle on automating support, drafting replies, and triaging tickets. Why not start your free trial today and see how much easier AI automation can be?